We review the asymptotic theory for standard errors in classical ordinary least squares (OLS) inverse or parameter estimation problems involving general nonlinear dynamical systems where sensitivity matrices can be used to compute the asymptotic covariance matrices. We discuss possible pitfalls in computing standard errors in regions of low parameter sensitivity and/or near a steady state solution of the underlying dynamical system.
Editor-in-Chief: Kabanikhin, Sergey I.
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Standard errors and confidence intervals in inverse problems: sensitivity and associated pitfalls
11. Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8205, USA.
2Email: htbanks@ncsu.edu
32. Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8205, USA.
4Email: sllawler@ncsu.edu
53. Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8205, USA.
6Email: slgrove@ncsu.edu
Citation Information: Journal of Inverse and Ill-posed Problems jiip. Volume 15, Issue 1, Pages 1–18, ISSN (Online) 1569-3953, ISSN (Print) 0928-0219, DOI: 10.1515/JIIP.2007.001, May 2007
Publication History:
- Published Online:
- 2007-05-31
Key Words: inverse problems,; standard errors,; dynamical systems,; parameter estimation,; sensitivity matrices,; asymptotic theory.
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